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Wireless multimedia sensor networks (WMSNs) are widely used in various fields where coverage control is a critical difficulty because multiple requirements need to be considered such as service quality and energy consumption. In this paper, we focus on the issues existing in coverage model and coverage control method, and propose an adaptive particle swarm optimization algorithm for solving the coverage control problem in the WMSNs. First of all, a 3D coverage model is developed with introducing the perceptual radius of sensors, while the optimal projection area of a single sensor is deduced. Hereafter, an adaptive particle swarm optimization is suggested to optimize the location information of sensors for reducing both perceptual overlapping areas and perceptual blind areas in the monitoring area. At last, a redundant node sleeping strategy is presented to reduce the number of working sensors. Simulation results have show that we can ensure better coverage as well as fewer sensors compared to the state-of-the-art frameworks.

Automatic modulation recognition (AMR) plays an important role in various communications systems. It has the ability of adaptive modulation and can adapt to various complex environments. Automatic modulation recognition is also widely used in orthogonal frequency division multiplexing (OFDM) systems. However, because the recognition accuracy of traditional methods to extract the features of OFDM signals is very limited. In order to solve these problems, many deep learning based AMR methods have been proposed to improve the recognition performance. However, most of these AMR methods neglect the harmful effect by carrier phase offset (PO) which often appears in realistic communications systems. Hence it is required to consider the PO effect for designing the OFDM system. Unlike conventional methods, we propose a convolutional neural network (CNN) based AMR method for considering PO in the OFDM system. The proposed method is used to eliminate the PO to achieve the high classification accuracy. Experiment results are provided to confirm the proposed method when comparing to conventional methods.

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We investigate into the problem of joint direction-of-departure (DOD) and direction-ofarrival (DOA) estimation in a multiple-input multiple-output radar, and a novel covariance tensor-based quadrilinear decomposition algorithm is derived in this paper. By taking into account the multidimensional structure of the matched array data, a fourth-order covariance tensor is formulated, which links the problem of joint DOD and DOA estimation to a quadrilinear decomposition model. A quadrilinear alternating least squares (QALSs) technique is applied to estimate the loading matrices, and thereafter automatically paired DODs and DOAs are obtained via the LSs fitting strategy. The proposed QALS algorithm can be regarded as an alternative to the direct parallel factor (PARAFAC) algorithm, which is more flexible than the latter since it can be easily expanded to scenario with spatially colored noise. Moreover, the proposed algorithm has much lower computational complexity than PARAFAC, especially in the presence of large snapshot. Numerical simulations verify the effectiveness of the proposed algorithm.

Automatic modulation recognition (AMR) plays an important role in various communications systems. It has the ability of adaptive modulation and can adapt to various complex environments. Automatic modulation recognition is also widely used in orthogonal frequency division multiplexing (OFDM) systems. However, because the recognition accuracy of traditional methods to extract the features of OFDM signals is very limited. In order to solve these problems, many deep learning based AMR methods have been proposed to improve the recognition performance. However, most of these AMR methods neglect the harmful effect by carrier phase offset (PO) which often appears in real communications systems. Hence it is required to consider the PO effect for designing the OFDM system. Unlike conventional methods, we propose a convolutional neural network (CNN) based AMR method for considering PO in the OFDM system. The proposed method is used to eliminate the PO to achieve the high classification accuracy. Experiment results are provided to confirm the proposed method when comparing to conventional methods.

With the rapid development of wireless communications technology, long term evolution (LTE) technology in unlicensed bands (LTE-U) can effectively solve the lack of spectrum resources. However, the competition in LTE-U and wireless fidelity (WiFi) will seriously interfere their communication quality, which making the friendly coexistence of LTE-U and WiFi becomes a hot research area. In this paper, we propose a classification algorithm based on deep learning to realize the identification of LTE-U and WiFi signal. Experiment results use mixed data at different signal to noise ratios (SNRs) and compare the classification results within two data forms. Experimental results show that our proposed deep learning-aided method can effectively distinguish LTE-U and WiFi signals and further achieve their friendly coexistence. INDEX TERMS Automatic LTE-U, WiFi, in-phase and quadrature, deep learning, unlicensed band.

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